easytorch 0.6

Easy Neural Network Experiments with pytorch

EasyTorch is a quick and easy way to start running pytorch experiments

As a phd student, I could not lose time on boilerplate neural network setups, so I started this sort-of-general framework to run experiments quickly. It consist of rich utilities useful for image manipulation as my research is focused on biomedical images. I would be more than happy if it becomes useful to any one getting started with neural netowrks.

Installation

1. Install latest pytorch and torchvision from Pytorch official website
2. pip install easytorch

Link to a full working example

Higlights

• A convenient framework to easily setup neural network experiments.
• Minimal configuration to setup a newu experimenton new dataset:
  • Use your choice of Neural Network architecture.
  • Create a python dictionary pointing to data ,ground truth, and mask directory(dataspecs.py).
  • Automatic k-fold cross validation.
  • Automatic logging/plotting, and model checkpointing.
  • Works on all sort of neural network related task.
  • GPU enabled metrics like precision, recall, f1, overlap, and confusion matrix with maximum GPU utilization.
  • Ability to automatically combine all the dataset with correct dataspecs and run on your favourite architecture.

Sample use case as follows:

import argparse

import dataspecs as dspec
from easytorch.utils.defaultargs import ap
from easytorch.runs import run, pooled_run
from classification import MyTrainer, MyDataset

ap = argparse.ArgumentParser(parents=[ap], add_help=False)

dataspecs = [dspec.DRIVE, dspec.STARE]
if __name__ == "__main__":
    run(ap, dataspecs, MyTrainer, MyDataset)
    pooled_run(ap, dataspecs, MyTrainer, MyDataset)

Training+Validation+Test

* $python main.py -p train -nch 3 -e 3 -b 2 -sp True

Only Test

* $python main.py -p test -nch 3 -e 3 -b 2 -sp True

References

Please cite us if you use this framework(easytorch) as follows: @misc{easytorch, author = {Khanal, Aashis}, title = {Quick Neural Network Experimentation}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, url = {https://github.com/sraashis/easytorch} }